Feature point location detection is a technique for detecting the location of a feature point of an organ such as an eye, a nose or a mouth from an image of a face or the like. The feature point location detection is an important technique for performing face authentication, facial expression recognition, or the like with high accuracy.
As a technique for detecting a feature point location of the face, there is known Active Appearance Model (AAM), for example (NPL 1). With AAM, a model for texture and shape of a face is constructed in a statistical method on the basis of a plurality of facial images and information on feature point locations previously input into the facial images. With AAM, the constructed model is fit to an image including the face to be detected.
That is, with AAM, a parameter of the model is repeatedly updated such that the facial image to be detected is closer to the facial image computed from the model, thereby detecting a feature point location. AAM has been variously extended since it was proposed. For example, there are many proposed methods for combining a plurality of models for detecting a profile or improvements for higher speed or higher accuracy.
AAM is known as being highly accurate when it is used for learning and detecting the faces of a few persons, such as learning the face of one person and constructing a specialized model of the person. To the contrary, AAM is known as being remarkably deteriorated in its performance when it is used for learning and detecting the faces of many persons under non-control (such as various illuminations and postures).
NPL 1 proposes, unlike an AAM model, a method for detecting a facial feature point by constructing a two-class discriminator for discriminating facial images into two classes of facial images acquired when all the facial feature points such as eyes, a nose, and a mouth are at the correct locations (which will be referred to as correct shape below) and facial images acquired when one or more of the facial feature points are out of the correct locations (which will be referred to as deviated shape below), and repeatedly updating a parameter of the model in order to enhance a discrimination score of the two-class discriminator.